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annotate pylearn/algorithms/tests/test_daa.py @ 1525:9c24a2bdbe90

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a test is too slow when run in debugmode, so we force fast_run in that case.
author Frederic Bastien <nouiz@nouiz.org>
date Fri, 09 Nov 2012 14:48:26 -0500
parents 70b061f142b2
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rev   line source
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1 #!/usr/bin/python
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2
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3 from pylearn import algorithms as models
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4 import theano
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5 import numpy
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6 import time
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7
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8 import pylearn.algorithms.logistic_regression
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9 from theano import config
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10 from pylearn.algorithms.stacker import Stacker
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11 from pylearn.algorithms.daa import SigmoidXEDenoisingAA
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12 from pylearn.algorithms.regressor import BinRegressor
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13 def test_train_daa(mode = config.mode):
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14
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89bc88affef0 Reverting out of changeset 633,634. Hopefully i did this write !
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15 ndaa = 3
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16 daa = Stacker([(SigmoidXEDenoisingAA, 'hidden')] * ndaa + [(BinRegressor, 'output')],
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17 regularize = False)
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18
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19 model = daa.make([4, 20, 20, 20, 1],
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20 lr = 0.01,
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21 mode = mode,
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22 seed = 10)
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23
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24 model.layers[0].noise_level = 0.3
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25 model.layers[1].noise_level = 0.3
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26 model.layers[2].noise_level = 0.3
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27
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28 # Update the first hidden layer
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29 for l in range(3):
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30 for i in range(10):
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31 model.local_update[l]([[0, 1, 0, 1]])
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32 model.local_update[l]([[1, 0, 1, 0]])
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33
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34 for i in range(10):
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35 model.update([[0, 1, 0, 1]], [[1]])
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36 model.update([[1, 0, 1, 0]], [[0]])
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37 print model.classify([[0, 1, 0, 1]])
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38 print model.classify([[1, 0, 1, 0]])
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40
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41 def test_train_daa2(mode = theano.Mode('c|py', 'fast_run')):
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42
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43 ndaa = 3
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44 daa = Stacker([(SigmoidXEDenoisingAA, 'hidden')] * ndaa + [(pylearn.algorithms.logistic_regression.Module_Nclass, 'pred')],
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45 regularize = False)
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46
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47 model = daa.make([4] + [20] * ndaa + [10],
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48 lr = 0.01,
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49 mode = mode,
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50 seed = 10)
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51
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52 for l in range(ndaa): model.layers[l].noise_level = 0.3
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53
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54 instances = [([[0, 1, 0, 1]], [1]), ([[1, 0, 1, 0]], [0])]
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55
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56 for l in range(ndaa):
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57 for i in range(10):
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58 for (input, output) in instances:
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59 model.local_update[l](input)
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61 for i in range(10):
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62 for (input, output) in instances:
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63 # model.update(input, output)
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64 print "OLD:",
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65 print model.validate(input, output)
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66 oldloss = model.update(input, output)
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67 print oldloss
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68 print "NEW:"
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69 print model.validate(input, output)
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70 print
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71
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72 print model.apply([[0, 1, 0, 1]])
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73 print model.apply([[1, 0, 1, 0]])
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78 if __name__ == '__main__':
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79 # print 'optimized:'
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80 # t1 = test_train_daa(theano.Mode('py', 'fast_compile'))
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81 # t1 = test_train_daa(theano.Mode('c|py', 'fast_run'))
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82 # print 'time:',t1
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83 # print
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84
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85 # print 'not optimized:'
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86 # t2 = test_train_daa(theano.Mode('c|py', 'fast_compile'))
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87 ## print 'time:',t2
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88
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89 # test_train_daa(theano.compile.Mode('c&py', 'merge'))
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90 # test_train_daa(theano.compile.Mode('c|py', 'merge'))
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91 test_train_daa(theano.compile.Mode('py', 'merge'))
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92
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93 test_train_daa2(theano.compile.Mode('c|py', 'merge'))